计算机应用 ›› 2014, Vol. 34 ›› Issue (11): 3210-3213.DOI: 10.11772/j.issn.1001-9081.2014.11.3210

• 网络与通信 • 上一篇    下一篇

基于蚁群算法的遥感影像传输资源调度方法

刘万军1,王晓宇1,曲海成1,2,孟煜1,姜庆玲3   

  1. 1. 辽宁工程技术大学 软件学院,辽宁 葫芦岛 125105;
    2. 哈尔滨工业大学 电子与信息工程学院, 哈尔滨 150006
    3. 铁岭师范高等专科学校 工学院,辽宁 铁岭 112008
  • 收稿日期:2014-06-01 修回日期:2014-07-04 出版日期:2014-11-01 发布日期:2014-12-01
  • 通讯作者: 王晓宇
  • 作者简介:刘万军(1959-),男,辽宁北镇人,教授,博士生导师,主要研究方向:数字图像处理、运动目标检测与跟踪;王晓宇(1991-),女,山西忻州人,硕士研究生,主要研究方向:遥感影像快速处理;曲海成(1981-),男,山东烟台人,博士研究生,主要研究方向:遥感影像高性能计算;孟煜(1990-),男,河北唐山人,硕士研究生,主要研究方向:图形图像处理、目标识别与跟踪。
  • 基金资助:

    国家863计划项目;国家自然科学基金资助项目

Transmission resource scheduling method for remote sensing images based on ant colony algorithm

LIU Wanjun1,WANG Xiaoyu1,QU Chenghai1,2,MENG Yu1,JIANG Qingling3   

  1. 1. Software Engineering Institute, Liaoning Technical University, Huludao Liaoning 125105, China;
    2. School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin Heilongjiang 150006, China;
    3. Engineering Institute, Tieling Normal College, Tieling Liaoning 112008,China
  • Received:2014-06-01 Revised:2014-07-04 Online:2014-11-01 Published:2014-12-01
  • Contact: WANG Xiaoyu

摘要:

针对遥感影像数据量大,多用户并发请求造成服务器负载加重,使遥感影像传输效率逐渐降低的问题,提出一种在多线服务器环境下分块调度遥感影像资源的策略。该策略采用改进的蚁群优化(IACO)算法,通过引入一个线路等待因子γ动态选择当前最优的线路进行传输,从而提高传输效率。对IACO、ACO、Max-min、Min-min和Random算法进行了对比实验,IACO算法在客户端的任务完成时间和服务器端的执行时间与其他算法相比均是最少的,且随着任务数目的增加,效果更明显;同时IACO算法的线路资源的利用率也更高。仿真结果表明:多线服务器分块调度策略与改进蚁群算法相结合,使遥感影像传输速度和线路资源利用率均有一定提高。

Abstract:

A block resource scheduling strategy for remote sensing images in multi-line server environment was proposed with the problems of huge amount of remote sensing data, heavy server load caused by multi-user concurrent requests which decreased the transmission efficiency of remote sensing images. To improve the transmission efficiency, an Improved Ant Colony Optimization (IACO) algorithm was used, which introduced a line waiting factor γ to dynamically select the optimal transmission lines. Intercomparison experiments among IACO, Ant Colony Optimization (ACO), Max-min, Min-min, and Random algorithm were conducted and IACO algorithm finished the tasks in the client and executed in the server with the shortest time, and the larger the amount of tasks, the more obvious the effect. Besides, the line resource utilization of IACO was the highest. The simulation results show that: combining multi-line server block scheduling strategy with IACO algorithm can raise the speed of remote sensing image transmission and the utilization of line resource to some degree.

中图分类号: